Target recognition and pattern classification involves the evaluation of new observations on the basis of past observations to distinguish targets or desired patterns from background clutter. This task is complicated by the complex and non-stationary nature of real world environments and often requires a multitude of classification techniques. Moreover, the computational overhead of many practical classification problems strain serial computer resources.
With respect to the inherent complexity of pattern classification problems, the nonstationary nature of many classification problems makes acquiring a representative data set for training a classifier difficult. The likelihood that the classification scheme would be able to recognize the desired pattern is small without representative training data. The likelihood that a single classification scheme would be able to recognize multiple desired patterns is small as well. This robustness issue is central to pattern recognition solutions in radar identification, speech recognition, and automatic target recognition. In radar identification, parameters describing a radar emitter vary dramatically, as warring parties deliberately change the frequencies and pulse repetition intervals from their peace-time values to disguise the identity of the emitter. In speech recognition, the meanings and sounds of words and phrases change as a function of the culture (or dialect), speaker, or context. In the automatic recognition of targets, targets exist in a vast array of settings, lighting conditions, times of the day and year, orientations, and positions.
With respect to the computational requirements, neural networks provide parallel computational implementations. These networks embody an approach to pattern recognition and classification based on learning. Example patterns are used to train these networks to isolate distinctions between the particular patterns and background clutter for proper classification.